The specification relates to gait analysis of a human by a robot unit.
Studies have shown that human gait is an important indicator of health, indicative of diabetes, neurological conditions, or fall predictions. Gait analysis generally requires similar conditions be reproduced in order to compare newly measured data with a previously determined baseline condition. Similar conditions are often captured in a clinical environment. A typical clinical environment may have one or more cameras placed around a walkway or treadmill, where the camera(s) and treadmill (if applicable) are coupled to and controlled by a computer. For improved accuracy, the camera position(s) may be substantially the same (e.g., have a stationary side view on of a subject on a treadmill, similar camera views of the subject along different portions of the walkway, etc.). The actual or simulated pathway is made long enough (e.g., ten feet) for a complete gait analysis to be performed on the target subject. Knowing the camera position and the length of the path allows for the reliable identification of medical issues related to the subject's movement.
While these types of controlled clinical environments for measuring gait may be functional, they have significant disadvantages. These types of clinical environments are generally restrictive because they require patrons, who are not all equipped or in suitable health, to go through the hassle of scheduling appointments and make dedicated trips to a clinical facility. These types of clinical environments are also expensive, as they require cleaning, maintenance, and specialized training and staffing by medical professionals.
Some treadmill-based setups may be installed and used by a target subject at home, such as the approach described by U.S. Pat. No. 8,002,672. This approach uses pressure sensors mounted to a treadmill to capture data. However, as with a clinical environment, a treadmill is inherently stationary and therefore the target subject is limited to having his/her gait analyzed in a single location (i.e., the treadmill and sensor configuration cannot be used to analyze gait in multiple locations). While it is possible for the pressure sensors to be mounted on other surfaces, such as a sidewalk, the cost of equipping a large environment is likely prohibitive to being able to gather data throughout the environment.
Some approaches use sensors mounted on the body of the subject to capture gait data. For example, U.S. Patent Application No. 2009/0030350 describes affixing an accelerometer to the subject for continuous gait analysis, and U.S. Patent Application No. 2008/0108913 describes mounting pressure sensors to shoes to analyze gait and detect falls of the subject. However, these types of wearable sensors have at least two significant drawbacks. First, subjects may dislike wearing the sensor, or simply forget to attach the sensor. Second, positioning the sensor repeatedly (e.g., day to day) during wear in a consistent manner is difficult to replicate. Further, while affixing the wearable device to clothing may help in overcoming the two issues discussed above, affixing the wearable device to clothing increases noise in the resulting data. Further, it can restrict the manner in which the equipped clothing can be handled and washed.
Some approaches utilize 3D cameras mounted in an environment to monitor subjects and capture gait data. For example, E. Stone and M. Skubic, “Unobtrusive, Continuous, In-Home Gait Measurement Using the Microsoft Kinect,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2925-2932, 2013 (Stone), describes a Microsoft Kinect™ skeletal tracker, which uses RGB-D sensors affixed to the ceilings of a target subject's home, to perform long-term gait monitoring of the target subject over a period of months. While this approach demonstrates that subjects could be recognized using the data gathered by the RGB-D sensors, the single camera placement restricted the amount of environments that could be monitored. In particular, only the rooms equipped with the tracker can be monitored, and as a result, the tracker is unable to monitor the health and activity of the target subject in other environments.
Some approaches capture gait data from multiple camera positions and/or angles. For example, M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster, “Full Body Gait Analysis with Kinect,” in the proceedings of the International Conference of the IEEE EM BS, San Diego Calif. USA, Aug. 28, 2000-Sep. 1, 2000 (“Gabel”), describes using an algorithm that recognizes people under a variety of camera angles. In particular, the algorithm uses the Microsoft Kinect™ skeletal tracker to recognize people under varied camera angles, including conditions where the gait was learned on one camera angle, and then evaluated on a separate camera angle. Gabel's approach disadvantageously requires full visibility of the subject and an unobstructed view from the camera in order to acceptably perform. Additionally, Gabel's approach exhibited degradation as the camera angle changed.
Some approaches use thermal image cameras to measure the gait of a target subject. For example, M. El-Yacoubi, A. Shaiek and B. Dorizzi, “HMM-based gait modeling and recognition under different walking scenarios,” 2011 International Conference on Multimedia Computing and Systems, 2011, describes using thermal imagery and Hidden Markov Models to extract silhouettes to model the gait of the subject. While, once trained, this approach can recognize various walking conditions of a target subject, it is focused on recognition and does not provide significant contributions to gait data collection or analysis methods.
Therefore, accessible and flexible technology is needed that is capable of monitoring the gait of a subject engaged in a variety of different activities and/or environments.